STUDY REGARDING THE SELECTION OF THE OPTIMUM PROCESS FLOW AT BEARING RINGS MANUFACTURING

Cezarina AFTENI, Mitica AFTENI, Gabriel-Radu FRUMUSANU, Florin SUSAC

Abstract


In the actual economical and technical environments, selecting the optimum manufacturing process from a several alternatives becomes the most important task for process designers, process planners and for process engineers also. The first selection step is encountered in quotation phase. In this paper a study concerning the structural identification of the process flow, based on the combinatorial optimization approach is proposed. The structural identification of the process flow, for each main process indicated as part of the flow diagram, becomes more and more complicated when more alternatives are available. A decision related to structural identification of the alternative process flow is requested in order to solve the problem occurred in the case when unpredicted events happen during the process. The proposed approach could be integrated and used also in the phases as: quotation, design, product development and related manufacturing processes ongoing. The database was built using data collected from industrial environment.   


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References


Guo K., Yang M., Zhu H., Application research of improved genetic algorithm based on machine learning in production scheduling, Neural Computing and Applications, vol. 32, no. 7. pp. 1857–1868, 2020.

Nehzati T., Ismail N., Application of Artificial Intelligent in Production Scheduling: a critical evaluation and comparison of key approaches, Proceedings of the 2011 International Conference on Industrial Engineering and Operations Management, pp. 28–33, 2011.

Frumusanu G-R., Afteni C., Epureanu A., Data-driven causal modelling of the manufacturing system, Transactions of Famena, vol. 45, no. 1, pp. 43–62, 2021.

Shao X., Li X., Gao L., Zhang C., Integration of process planning and scheduling-A modified genetic algorithm-based approach, Computers & Operations Research, vol. 36, no. 6, pp. 2082–2096, 2009.

Izadi L., Ahmadizar F., and Arkat J., A Hybrid Genetic Algorithm for Integrated Production and Distribution Scheduling Problem with Outsourcing Allowed, International Journal of Engineering, vol. 33, no. 11, pp. 2285–2298, 2020.

Ölçer A-I., A hybrid approach for multi-objective combinatorial optimisation problems in ship design and shipping, Computers & Operations Research, vol. 35, no. 9, pp. 2760–2775, 2008.

Chen K‐S, Yu C‐M., Hsu T.‐H., Cai S-R., Chiou K-C., A model for evaluating the performance of the bearing manufacturing process, Applied Science, vol. 9, no. 3105, 2019.

Afteni C., Holistic optimization of manufacturing process, PhD Thesis, “Dunarea de Jos” University of Galati, series I 4: Industrial Engineering,, no. 70, 2020.

Afteni M., Paunoiu V., Afteni C., Frumusanu G-R., Structural identification of the bearing manufacturing process – Case-study, IOP Conference Series: Materials Science and Engineering, vol. 968, no. 012015, pp. 1–11, 2020.

https://ro.wikipedia.org/wiki/Combinatorica.

Korte B., Vygen J., Combinatorial Optimization - Theory and Algorithms, Springer, vol. 21, pp. 1–596, 2018.

Odili J-B., Combinatorial optimization in science and engineering, Current Science Association, vol. 113, no. 12, pp. 2268–2274, 2017.

Gasse M., Chételat D., Ferroni N., Charlin L., Lodi A., Exact Combinatorial Optimization with Graph Convolutional Neural Networks, 33rd Conference on Neural Information Processing Systems Datasets and Benchmarksur, pp. 1–13, 2019.

Kianfar K., Branch‐and‐Bound Algorithms, Wiley Encyclopedia of Operations Research and Management Science, 2011.

Duan S., Jiang S., Dai H., Wang L., He Z., The applications of hybrid approach combining exact method and evolutionary algorithm in combinatorial optimization, Journal of Computational Design and Engineering, vol. 10, no. 3, pp. 934–946, 2023.

Lodi A., Zarpellon G., On learning and branching: a survey, TOP, vol. 25, no. 2, pp. 207–236, 2017.

Dogan G., Mehmet I., Chitariu D-F., Dumitraș C-G., Crîșmaru V-I., FEA Modelling of the Combined Hard Turning- Rolling Process Used at Bearing Rings, MATEC Web Conferences, vol. 343, no. 02004, 2021.

Mehmet I., Dogan G., Chitariu D-F., Dumitraș C., Negoescu F., Research on advances in roller bearing manufacturing, IOP Conf. Series: Materials Science and Engineering, vol. 1182, no. 012045, 2021.

Tiwari A., Vasnani H., Labana M., The process for manufacturing of ball bearing and effect of material in bearing life, International Journal of Advance Research in Science and Engineering, vol. 6, no. 1, pp. 235–252, 2017.

Arya S., Singh M-P., Bhargava M. Development of Mathematical Model and Process Optimization of Deep Groove Ball Bearing, International Conference on Advancements in Computing & Management, 2019.


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